Unsupervised Learning

Unsupervised learning is a type of machine learning where algorithms learn patterns from unlabeled data without explicit supervision or predefined outcomes. It focuses on discovering hidden structures, such as clusters, associations, or anomalies, within the data. Common techniques include clustering, dimensionality reduction, and density estimation.

Also known as: Unsupervised ML, Unsupervised Machine Learning, Clustering, Dimensionality Reduction, Anomaly Detection

Why learn Unsupervised Learning?

Developers should learn unsupervised learning for tasks like customer segmentation, anomaly detection in cybersecurity, or data compression in image processing. It is essential when labeled data is scarce or expensive, enabling insights from raw datasets in fields like market research or bioinformatics.

Learning Resources

• docs: Scikit-learn Unsupervised Learning Documentation
• course: Coursera: Unsupervised Machine Learning
• tutorial: Towards Data Science: Unsupervised Learning Tutorial
• video: YouTube: Unsupervised Learning Explained